Segmentation of Blood Cell Images Using Hybrid K-means with Cluster Center Estimation Technique
A.A. Mariena1, J.G.R. Sathiaseelan2

1A.A. Mariena, Research Scholar, Department of Computer Science, Bishop Heber College, Trichy (Tamil Nadu), India.
2J.G.R. Sathiaseelan, Associate Professor and Head, Department of Computer Science, Bishop Heber College, Trichy (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 160-163 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10260982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1026.0982S1119
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Abstract: Image segmentation plays a predominant role in the field of image processing. k- Means clustering is one of the most powerful algorithms for medical image segmentation. However, the randomly selected cluster number and initial centroids cause inconsistency in the image segmentation results. To overcome this limitation we have proposed a combined approach namely Hybrid K-Means with Cluster Center Estimation (HKMCCE) for image segmentation. The proposed technique use histogram peaks of the image to find the cluster number and initial cluster centers automatically. Moreover, it requires lessuser interaction to determine k-means initialization parameters. The performance of the proposed technique is compared with traditional segmentation methods and it yields better results with less execution time.
Keywords: K-Means, HKMCCE, Histogram.
Scope of the Article: Image Security